CN112904277B - Tunnel surrounding rock breaking point positioning method based on improved gray wolf algorithm - Google Patents
Tunnel surrounding rock breaking point positioning method based on improved gray wolf algorithm Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
Abstract
The invention discloses a tunnel surrounding rock breaking point positioning method based on an improved wolf algorithm, aiming at the defects of lower efficiency and poorer precision of the traditional acoustic emission positioning algorithm, the invention provides a method for searching the space coordinates of the surrounding rock breaking point by taking the sum of residual absolute values of travel time equations as an adaptability function of the wolf algorithm on the basis of acoustic emission monitoring data, and simultaneously, in order to overcome the problem of poor local searching capability of the wolf algorithm, a population memory elimination mechanism is introduced to improve the wolf algorithm, and a cosine function decreasing form is adopted to improve convergence factors. The method and the system have the advantages that the calculation efficiency, accuracy and stability of the positioning algorithm are better improved, the occurrence position of cracking and breaking of surrounding rock in the tunnel excavation process is displayed in an intuitive and image mode, and a rapid and accurate real-time monitoring and early warning means is provided for tunnel construction.
Description
Technical Field
The invention relates to the technical field of tunnel safety monitoring, in particular to a tunnel surrounding rock breaking point positioning method based on an improved gray wolf algorithm.
Background
In the tunnel excavation process, serious personal and property losses can be caused by instability damage of surrounding rocks, so that the method has important engineering significance for monitoring the cracking of the surrounding rocks in real time in the construction process, and can provide basis for disaster prevention and control, thereby avoiding the occurrence of disasters.
The acoustic emission monitoring technology not only can detect the generation of microcracks in the rock in real time, but also can realize more accurate positioning, and is widely applied to geotechnical engineering geological disaster prevention and control. However, the conventional acoustic emission positioning algorithm has respective limitations, low efficiency and poor precision, and needs to be improved.
With the rapid development of computer technology, more and more intelligent algorithms are developed successively, and the intelligent algorithms are widely focused by students once coming out due to the advantages of strong searching capability, rapid convergence, simple structure, easy realization and the like, and are successfully applied to the fields of parameter optimization, image processing and the like, but are still freshly applied to the field of acoustic emission positioning.
In conclusion, the traditional acoustic emission positioning method is improved by using the intelligent algorithm, so that the acoustic emission positioning of the tunnel surrounding rock fracture can be realized more quickly and accurately, and more reliable monitoring data is provided for the tunnel engineering disaster prevention and control work.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a tunnel surrounding rock breaking point positioning method based on an improved gray wolf algorithm, the position of a surrounding rock breaking point is positioned by adopting an intelligent algorithm through acoustic emission monitoring data, and the efficiency, accuracy and stability of positioning are improved.
The specific technical scheme is as follows:
in a first aspect, a method for positioning a breaking point of a surrounding rock of a tunnel based on an improved wolf algorithm is provided, which comprises the following steps:
collecting P-wave signals generated when surrounding rock of a tunnel is broken through a plurality of acoustic emission probes which are uniformly distributed, and recording the receiving time of each acoustic emission probe for receiving the P-wave signals;
and positioning the breaking point coordinates corresponding to the breaking points through a gray wolf algorithm based on the space coordinates of each acoustic emission probe in the positioning space and the receiving time of the received P-wave signals.
With reference to the first aspect, in a first implementation manner of the first aspect, the locating, by a gray wolf algorithm, the rupture point coordinates corresponding to the rupture point includes:
randomly selecting a plurality of space points in a positioning space, and determining the coordinates of each space point;
calculating the fitness function value of each space point through the coordinates of the space point, the space coordinates corresponding to each acoustic emission probe and the receiving time;
selecting m optimal space points from all the space points according to the fitness function value;
updating the fitness function value of each space point based on the coordinates of the optimal space point;
judging whether the ending condition is met or not, if not, re-selecting the fitness function value of each space point of the m optimal space points, iterating in the way until the ending condition is met, and selecting the coordinate corresponding to the minimum fitness function value as the coordinate of the breaking point.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the calculating an fitness function value of each spatial point includes:
calculating travel time equation residual errors of the space points relative to each acoustic emission probe according to the coordinates of the space points, the space coordinates corresponding to the acoustic emission probes and the receiving time;
and determining the fitness function value of the space point according to the travel time equation residual error of the space point relative to each acoustic emission probe.
With reference to the first implementation manner of the first aspect, in a third implementation manner of the first aspect, the following method is adopted to select m optimal spatial points:
sequencing all the fitness function values obtained by calculation according to the sequence from small to large;
and selecting the space points corresponding to the m fitness function values as optimal space points according to the arrangement sequence.
With reference to the first implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the fitness function value of the spatial point is updated by adopting the following method:
calculating the position distance between the space point and each optimal space point and the convergence factor corresponding to the current iteration step number;
determining candidate position coordinates of the space points according to the convergence factor and the position distance;
updating coordinates of the space points according to the candidate position coordinates;
and calculating to obtain the fitness function value of the updated space point through the updated space point coordinates, the space coordinates corresponding to each acoustic emission probe and the receiving time.
With reference to the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect, based on a preset total iteration step number and a current iteration step number, determining a convergence factor corresponding to the current iteration step number by using a cosine function.
With reference to the first aspect, in a sixth implementation manner of the first aspect, after updating the fitness function value of each spatial point, the method further includes: and performing space point replacement through a population memory elimination mechanism.
With reference to the first aspect, in a seventh implementation manner of the first aspect, the method further includes:
recording the breaking point coordinates of each determined breaking point;
and drawing a three-dimensional image according to the recorded data and the space coordinates of all the acoustic emission probes.
In a second aspect, a tunnel surrounding rock breaking point positioning method based on an improved wolf algorithm is provided, which comprises the following steps:
a plurality of acoustic emission probes are uniformly arranged on the inner wall of the surrounding rock of the tunnel;
determining the space coordinates of each acoustic emission probe in the positioning space;
recording the moment when each acoustic emission probe receives a P-wave signal generated by surrounding rock fracture;
and positioning the breaking point coordinates corresponding to the breaking points through a gray wolf algorithm based on the space coordinates of each acoustic emission probe in the positioning space and the receiving time of the received P-wave signals.
With reference to the second aspect, in a first implementation manner of the second aspect, the determining spatial coordinates of each acoustic emission probe in the positioning space includes:
constructing a three-dimensional rectangular coordinate system corresponding to the positioning space by taking the center of the tunnel face as an origin;
and determining the space coordinates of the acoustic emission probes according to the relative positions of each acoustic emission probe and the center of the face.
The beneficial effects are that:
(1) The gray wolf algorithm in the intelligent algorithm is utilized, acoustic emission monitoring data are combined, and the sum of residual absolute values of travel time equations is used as an adaptability function of the gray wolf algorithm to search space coordinates of surrounding rock fracture points, so that the calculation efficiency, accuracy and stability of the traditional positioning algorithm are improved.
(2) The linear decreasing form of the convergence factor in the traditional wolf algorithm is changed into the decreasing form of the cosine function, so that the wolf algorithm has higher global searching capability in the early stage and higher local searching capability in the later stage, and is more in line with the actual searching process.
(3) In order to solve the problem of poor local searching capability of the wolf algorithm, a population memory elimination mechanism is introduced to improve the wolf algorithm, the optimal individuals of the population are always reserved in the iteration process, and the worst individuals are eliminated, so that the local searching capability is improved, and the searching efficiency is also accelerated.
(4) According to the optimal space point position coordinates obtained by searching the improved gray wolf algorithm, a three-dimensional acoustic emission positioning schematic diagram of the surrounding rock fracture of the tunnel is drawn in real time, the occurrence position of the surrounding rock fracture in the tunnel excavation process can be shown for engineering construction personnel intuitively and vividly, and the effect of real-time monitoring and early warning is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described. Throughout the drawings, the elements or portions are not necessarily drawn to actual scale.
FIG. 1 is a flow chart of a method for locating a breaking point according to an embodiment of the present invention;
FIG. 2 is a flow chart of the gray wolf algorithm shown in FIG. 1 for determining the coordinates of a rupture point;
FIG. 3 is a flow chart of updating fitness function values of other spatial points shown in FIG. 2;
FIG. 4 is a flow chart of determining a rupture point according to the gray wolf algorithm provided by an embodiment of the present invention;
FIG. 5 is a flow chart of a method for locating a rupture point according to an embodiment of the present invention;
fig. 6 is a flowchart of a method for locating a breaking point according to an embodiment of the present invention.
Detailed Description
Embodiments of the technical scheme of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and thus are merely examples, and are not intended to limit the scope of the present invention.
The method for locating the breaking point of the surrounding rock of the tunnel based on the improved gray wolf algorithm as shown in fig. 1 comprises the following steps:
collecting P-wave signals generated when surrounding rock of a tunnel is broken through a plurality of acoustic emission probes which are uniformly distributed, and recording the moment when each acoustic emission probe receives the P-wave signals;
and step two, positioning the coordinates of the breaking points corresponding to the breaking points through a gray wolf algorithm based on the space coordinates of each acoustic emission probe in the positioning space and the receiving time of the received P-wave signals.
Specifically, when a local crack of each place of the surrounding rock of the tunnel is broken, the acoustic emission probes receive a P-wave signal in real time, the space coordinates of each acoustic emission probe and the time when the P-wave signal is received are output to an external computer, and the computer can position the breaking point coordinates of the breaking points by adopting the gray wolf algorithm in the existing intelligent algorithm according to the space coordinates of each acoustic emission probe and the time when the P-wave signal is received, so that the calculation efficiency, accuracy and stability of the positioning algorithm are improved.
In this embodiment, preferably, as shown in fig. 2, the locating, by a wolf algorithm, the fracture point coordinates corresponding to the fracture point includes:
step 1, randomly selecting a plurality of space points in a positioning space, and determining the coordinates of each space point;
step 2, calculating the fitness function value of each space point through the coordinates of the space point, the space coordinates corresponding to each acoustic emission probe and the receiving time;
step 3, selecting m optimal space points from all space points according to the fitness function value;
step 4, updating the fitness function value of each space point based on the coordinates of the optimal space point;
and 5, judging whether the ending condition is met, if not, re-selecting the fitness function value of each space point of the m optimal space points, iterating in the way until the ending condition is met, and selecting the coordinate corresponding to the minimum fitness function value as the coordinate of the breaking point.
In particular, the method comprises the steps of,
firstly, the computer can randomly initialize three-dimensional position coordinates of 100 space points, namely, initialize a gray wolf population, in the monitoring range of each acoustic emission probe, namely, in a positioning space, by using a rand function, wherein the coordinates of each space point are x j ,y j ,z j ，j＝1,2,3…,100；
And secondly, calculating the fitness function value of each space point, namely the fitness of each individual wolf by the computer through the corresponding coordinates, the space coordinates corresponding to each acoustic emission probe and the receiving time.
In this embodiment, in order to improve the accuracy and stability of positioning calculation, the travel time equation residual error of the space point relative to each acoustic emission probe is calculated according to the coordinates of the space point, the space coordinates corresponding to the acoustic emission probes, and the receiving time, and the fitness function value of the space point is determined according to the travel time equation residual error of the space point relative to each acoustic emission probe, where the specific calculation formula is as follows:
wherein X is i ,Y i ,Z i For the spatial position coordinates of the acoustic emission probe, i=1, 2,3 …, N, t i For the receiving moment of the P wave signal received by the acoustic emission probe, vp is the P wave speed, t 1 The receiving moment of the acoustic emission probe which receives the P-wave signal first.
Then, the computer selects 3 (m=3) space points with the smallest fitness function value as the optimal space points by comparing the fitness function values of the space points, namely 3 head wolves alpha, beta and delta.
In this embodiment, in order to accelerate the computing efficiency of the computer, the following method is adopted to select the optimal spatial point:
sequencing all the fitness function values obtained by calculation according to the sequence from small to large;
and selecting the space points corresponding to the m fitness function values as optimal space points according to the arrangement sequence.
Specifically, the computer uses the sort function to sequence all fitness function values from small to large, and then selects the spatial point corresponding to the fitness function value of the first 3 bits from the selected spatial point as the optimal spatial point according to the sequence.
Then, as shown in fig. 3, the computer updates the fitness function value of the spatial point by the following method:
step 4-1, calculating the position distance between the space point and each optimal space point and the convergence factor corresponding to the current iteration step number;
step 4-2, determining candidate position coordinates of the space points according to the convergence factor and the position distance;
step 4-3, updating coordinates of the space points according to the candidate position coordinates;
and 4-4, calculating to obtain the fitness function value of the updated space point through the updated space point coordinates, the space coordinates corresponding to each acoustic emission probe and the receiving time.
Specifically, first, the distances between each other spatial point, i.e., candidate wolf, and 3 optimal spatial points α, β, δ are calculated as follows:
r is a random number within the range of 0, 1.
Then according to the distance from the space point to 3 optimal space points alpha, beta and delta, 3 candidate position coordinates of each candidate wolf are determined according to the following calculation formula:
a is a convergence factor decreasing from 2 to 0, in this embodiment, in order to enable the positioning method to have higher global searching capability in the early stage and higher local searching capability in the later stage, so as to be more suitable for an actual searching process, in this embodiment, a cosine function is used to determine the convergence factor corresponding to the current iteration step number, and a specific calculation formula is as follows:
where t is the current iteration step number and maxt is the total iteration step number.
After determining 3 candidate position coordinates corresponding to the space point, calculating coordinates after the space point update by adopting the following calculation formula:
and finally, calculating by using the coordinates updated by the space points, the space coordinates corresponding to each acoustic emission probe and the receiving time by using the calculation formula to obtain the fitness function value updated by the space points.
And finally, the computer judges whether the optimal space point, namely the fitness function value of the head wolf is smaller than a preset error or the current iteration number is larger than a preset total iteration number, if not, the step 3 is returned, and if so, the coordinate corresponding to the minimum fitness function value is recorded as the coordinate of the breaking point.
The second embodiment is substantially the same as the first embodiment, and the main difference is that: as shown in fig. 4, in this embodiment, preferably, after updating the fitness function value of each spatial point, the method further includes:
and 6, replacing space points through a population memory elimination mechanism.
Specifically, after the fitness function values of all the space points are updated, the computer ranks the space points by using the sort function, finds 3 space points with the largest fitness function values after updating, and replaces the space points with the largest fitness function values by using the 3 optimal space points before updating, namely the 3 top wolves. Therefore, optimal individuals of the population are always reserved in the iteration process, and the worst individuals are eliminated, so that the local searching capability is improved, and the searching efficiency is also improved.
In the third embodiment, as shown in fig. 5, in this embodiment, it is preferable that the method further includes:
and 7, recording the breaking point coordinates of the breaking points determined each time, and drawing a three-dimensional image according to the recorded data and the space coordinates of all the acoustic emission probes.
Specifically, after each time the computer determines a breaking point coordinate, a three-dimensional acoustic emission positioning schematic diagram of the breaking of the surrounding rock of the tunnel can be drawn in real time according to the breaking point coordinate and the space coordinates of all acoustic emission probes, so that the occurrence position of the breaking of the surrounding rock in the tunnel excavation process can be shown for engineering construction personnel more intuitively and vividly, and the effect of real-time monitoring and early warning is achieved.
The method for locating the breaking point of the surrounding rock of the tunnel based on the improved gray wolf algorithm as shown in fig. 6 comprises the following steps:
s1, uniformly arranging a plurality of acoustic emission probes on the inner wall of surrounding rock of a tunnel;
s2, determining the space coordinates of each acoustic emission probe in a positioning space;
s3, recording the time when each acoustic emission probe receives a P-wave signal generated by surrounding rock fracture;
and S4, positioning the coordinates of the breaking points corresponding to the breaking points through a gray wolf algorithm based on the space coordinates of each acoustic emission probe and the receiving time of the received P-wave signals.
In particular, the method comprises the steps of,
firstly, a plurality of acoustic emission probes can be uniformly arranged on the inner wall of a tunnel manually;
secondly, constructing a three-dimensional rectangular coordinate system of a positioning space by taking the center of the tunnel face as an origin, establishing a tunnel three-dimensional model, and determining the space coordinate of each acoustic emission probe in the positioning space according to the actual arrangement position of the acoustic emission probe;
then, recording the moment when each acoustic emission probe receives a P-wave signal generated by surrounding rock fracture;
finally, based on the space coordinates of each acoustic emission probe and the receiving time of the received P-wave signal, the corresponding breaking point coordinates of the breaking point are positioned by adopting a gray-wolf algorithm, and the specific flow of positioning the breaking point coordinates by adopting the gray-wolf algorithm is the same as that of the positioning method, so that the detailed description is omitted.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (9)
1. The tunnel surrounding rock breaking point positioning method based on the improved gray wolf algorithm is characterized by comprising the following steps of:
collecting P-wave signals generated when surrounding rock of a tunnel is broken through a plurality of acoustic emission probes which are uniformly distributed, and recording the receiving time of each acoustic emission probe for receiving the P-wave signals;
based on the space coordinates of each acoustic emission probe in the positioning space and the receiving time of the received P-wave signal, positioning the coordinates of the breaking point corresponding to the breaking point through a gray wolf algorithm;
the locating the corresponding breaking point coordinates of the breaking point through the gray wolf algorithm comprises the following steps:
randomly selecting a plurality of space points in a positioning space, and determining the coordinates of each space point;
calculating the fitness function value of each space point through the coordinates of the space point, the space coordinates corresponding to each acoustic emission probe and the receiving time, wherein the specific calculation formula is as follows:
wherein X is i ,Y i ,Z i For spatial position coordinates of the acoustic emission probe, i=1, 2, 3..n, t i For the receiving moment of the P wave signal received by the acoustic emission probe, vp is the P wave speed, t 1 The receiving moment of the acoustic emission probe for receiving the P-wave signal is the first time;
selecting m optimal space points from all the space points according to the fitness function value;
updating the fitness function value of each space point based on the coordinates of the optimal space point;
judging whether the ending condition is met or not, if not, re-selecting the fitness function value of each space point of the m optimal space points, iterating in the way until the ending condition is met, and selecting the coordinate corresponding to the minimum fitness function value as the coordinate of the breaking point.
2. The tunnel surrounding rock fracture point positioning method based on the improved wolf algorithm of claim 1, wherein the calculating the fitness function value of each spatial point comprises:
calculating travel time equation residual errors of the space points relative to each acoustic emission probe according to the coordinates of the space points, the space coordinates corresponding to the acoustic emission probes and the receiving time;
and determining the fitness function value of the space point according to the travel time equation residual error of the space point relative to each acoustic emission probe.
3. The tunnel surrounding rock fracture point positioning method based on the improved wolf algorithm as claimed in claim 1, wherein the following method is adopted to select m optimal space points:
sequencing all the fitness function values obtained by calculation according to the sequence from small to large;
and selecting the space points corresponding to the m fitness function values as optimal space points according to the arrangement sequence.
4. The tunnel surrounding rock fracture point positioning method based on the improved wolf algorithm as claimed in claim 1, wherein the fitness function value of the space point is updated by adopting the following method:
calculating the position distance between the space point and each optimal space point and the convergence factor corresponding to the current iteration step number;
determining candidate position coordinates of the space points according to the convergence factor and the position distance;
updating coordinates of the space points according to the candidate position coordinates;
and calculating to obtain the fitness function value of the updated space point through the updated space point coordinates, the space coordinates corresponding to each acoustic emission probe and the receiving time.
5. The tunnel surrounding rock breaking point positioning method based on the improved wolf algorithm of claim 4, wherein the convergence factor corresponding to the current iteration step number is determined by adopting a cosine function based on the preset total iteration step number and the current iteration step number.
6. The tunnel surrounding rock breaking point positioning method based on the improved wolf algorithm according to claim 1, further comprising, after updating the fitness function value of each spatial point: and performing space point replacement through a population memory elimination mechanism.
7. The tunnel surrounding rock breaking point positioning method based on the improved wolf algorithm of claim 1, further comprising:
recording the breaking point coordinates of each determined breaking point;
and drawing a three-dimensional image according to the recorded data and the space coordinates of all the acoustic emission probes.
8. The tunnel surrounding rock breaking point positioning method based on the improved gray wolf algorithm is characterized by comprising the following steps of:
a plurality of acoustic emission probes are uniformly arranged on the inner wall of the surrounding rock of the tunnel;
determining the space coordinates of each acoustic emission probe in the positioning space;
recording the moment when each acoustic emission probe receives a P-wave signal generated by surrounding rock fracture;
based on the space coordinates of each acoustic emission probe and the receiving time of the received P-wave signal, locating the coordinates of the breaking point corresponding to the breaking point through a gray wolf algorithm;
the positioning of the rupture point coordinates corresponding to the rupture point by the gray wolf algorithm comprises:
randomly selecting a plurality of space points in a positioning space, and determining the coordinates of each space point;
calculating the fitness function value of each space point through the coordinates of the space point, the space coordinates corresponding to each acoustic emission probe and the receiving time;
selecting m optimal space points from all the space points according to the fitness function value;
and updating the fitness function value of each space point based on the coordinates of the optimal space point, wherein the specific calculation formula is as follows:
wherein X is i ,Y i ,Z i For spatial position coordinates of the acoustic emission probe, i=1, 2, 3..n, t i For the receiving moment of the P wave signal received by the acoustic emission probe, vp is the P wave speed, t 1 For the first sound of receiving P-wave signalsThe receiving moment of the transmitting probe;
judging whether the ending condition is met or not, if not, re-selecting the fitness function value of each space point of the m optimal space points, iterating in the way until the ending condition is met, and selecting the coordinate corresponding to the minimum fitness function value as the coordinate of the breaking point.
9. The method for locating a tunnel surrounding rock fracture point based on the improved wolf algorithm of claim 8, wherein the determining the spatial coordinates of each acoustic emission probe in the locating space comprises:
constructing a three-dimensional rectangular coordinate system corresponding to the positioning space by taking the center of the tunnel face as an origin;
determining the relative position of each acoustic emission probe and the center of the face;
and determining the space coordinates of each acoustic emission probe according to the corresponding relative positions.
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| 基于GWO-ELM的逆变器开路故障诊断;姚芳;姜涛;刘明宇;董超群;郑帅;;电源学报(第01期) * |
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| CN112904277A (en) | 2021-06-04 |
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